Design and implementation of power conditioning for distribution network V2G to electric vehicle and DC charging system

Latha, K. Sree; Swarupa, M. Lakshmi

doi:10.1063/5.0020035

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Figure 4 depicts the topology of the QBBC. This topology eliminates the need for additional passive components like inductors and capacitors, setting it apart from the classical Boost quadratic converter [15]. The QBBC shows a static voltage gain, employing a quadratic function for both Buck and Boost operating modes.…”

Section: Quadratic Bidirectional Boost /Buck Dc-dc Convertermentioning

confidence: 99%

“…A novel approach has been developed to identify shaded areas throughout the day using a shading matrix, which illustrates the extent of shading in various areas each hour, such as roads or parking spots [14]. Additionally, researchers have assessed the accuracy of two methods: one involving modelling and another based on real-life measurements [15]. Moreover, numerous studies have explored optimal routes for solar-powered electric vehicles [16].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

G. Divya

2024

jes

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This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

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Section: Quadratic Bidirectional Boost /Buck Dc-dc Convertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

G. Divya

2024

jes

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“…Even though milage is very depressing, in urban driving about one half to one third of energy needed is compensated by braking. Specially the regenerative braking will help to increase the fuel economy to 30% with regenerative braking in hybrid and 8-26% may be expected in full electric vehicle [12][13] [14]. The performance of an electric vehicle mainly depends on electric motor drive efficiency and operation.…”

Section: Mtalab -Simulink Model Of Electric Vehiclementioning

confidence: 99%

Design and mathematical modelling of Electric Vehicle

Rajitha,

Swarupa,

Sree Latha

et al. 2024

E3S Web Conf.

Self Cite

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A model which is based on design will reduce the development cost of the system. There are different phases associated with complex models for systems like Electric Vehicles, these include Functional Specification definitions, Design specification, Test and authenticate and implementation. The case study done in this paper will be including only first two stages. The results will help to understand how the system designer can take decisions using simulation output. Simulation is a very complicated task which can represent simple design with precision and complex design with great accuracy that is almost near to the practical model. There is a compromise when making decisions with respect to precision and design complexity. It is always recommended to consider accurate models but when parameters are considered it is very difficult to determine parameters preferably in first stage of the model development and even the simulation of these accurate model is very slow. Hence a more detailed level of simulation model is needed. So, the system designer needs a complete overview of system power flow in the initial level of modelling. In the second level there is a need of designing more accurate model of different systems in which choosing an appropriate parameter for energy management system and power converters is needed. In this paper it can be clarified that a detailed design will validate the system’s behaviour with great accuracy and to conduct other adoptions as requires.

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“…This inconvenience for users and is not conducive to the management of charging stations. Existing literature mainly focuses on aspects such as the quantity [1], construction, design, operation, and fault detection of charging stations [2][3], but there is little research on measurement. To overcome the problems of traditional onsite measurement and detection methods, reference [4] proposed the comparison method of metering modules and the big data screening method.…”

Section: Introductionmentioning

confidence: 99%

Prediction and analysis of relative error in electric vehicle charging stations based on an improved ConvFormer model

Liwen,

Qingquan,

Xing

et al. 2023

Preprint

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In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE and MSE, are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 hours is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 57.05% reduction in MAE compared to the Transformer model and a 51.48% reduction compared to LSTM, and a 66.95% reduction in MSE compared to the Transformer model and approximately 62.23% reduction compared to LSTM.

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Design and implementation of power conditioning for distribution network V2G to electric vehicle and DC charging system

Cited by 9 publications

References 4 publications

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

Design and mathematical modelling of Electric Vehicle

Prediction and analysis of relative error in electric vehicle charging stations based on an improved ConvFormer model

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